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Razi, a Persian scientist in the 10th century. Drawing: Louis Figuer's "Vies des Savants Moyen Age," 1867. Photo: Hulton/Getty
Ever since science became a formal discipline some five centuries ago, academic research — a fundamental driver of innovation — has, on and off, seemed broken: Scientists have cranked out too many incremental advances, fallen behind on the best research in their field and produced unreplicable work.
Why it matters: The U.S. government spends billions on academic research each year — and companies toss in billions more. Yet science can appear to be treading water, turning out a similar scale of breakthroughs as when funding was lower and the number of researchers smaller.
One problem: A combination of factors — higher funding, faster computers and far more data — results in researchers spending much precious time sorting through a relentless avalanche of scholarship.
Language is the core of the problem. Papers are ostensibly written for other scientists to read and understand, but the sheer volume of information means the scientists are in serious need of help.
The answer, some think, is simply to do a better job of sorting, cataloging and assessing papers as they are published.
A first step is to automatically check facts and compare results against previous work.
This is the tip of the arrowhead.
"The model of referring to a text-based paper for the purpose of communicating experimental results will probably disappear."— Robert Murphy, professor of computational biology, Carnegie Mellon
But, but, but: This automated utopia is a long way off. Natural language processing is still hard for computers, and a system trained to understand papers in a particular field might fail when reading another field’s work.
Illustration: Sarah Grillo/Axios
Last week, we wrote about the surprisingly crowded field of artificial intelligence, in which a lot of talent has been snapped up by Big Tech, but is also distributed along a long tail of smaller companies around the world.
Kaveh writes: Now, a new report gets more specific on what AI talent is turning out — and finds that European AI experts are producing a lot more than those in the U.S. The same report finds that the field is heavily dominated by men.
Why it matters: AI, more than most technologies, reflects the perspectives and biases of its architects. "Diversity remains a central challenge of the field," tweeted Terah Lyons, a co-author of the report and director of the Partnership on AI.
Much of the second annual AI Index — a project from Stanford's Human-Centered AI Institute — focuses on who is creating the technology.
By the numbers:
The big question: Who is not in the room? From the report:
Go deeper: AI talent is not monopolized quite yet
You've been completely preoccupied? Never mind, here are the top Axios Future articles last week:
1. Dollar stores, everywhere: Thriving no matter what
2. The healthy macro impact of low joblessness: A much-overlooked impact
3. Quantum's killer app: A quest for a user case
4. A new era of U.S.-China hostility: Everyone a target
5. What ails us? A new look at the upheaval all around us
World GDP by country, 1981-2017 (Kash Sirinanda — Efuturists) (animation)
The widening world of refugees (Stef Kight et al. — Axios)
Rethinking the purpose of the corporation (Martin Wolf — FT)
Making the world safer for autocrats (Walt Bogdanich, Michael Forsythe — NYT)
The complete Russian guide to disinformation (Craig Timberg, Tony Romm — WP)
A commuter found KiwiBot riding along with her. Screenshot of tweet by BelatedKait
Robots have feelings, too — or at least UC Berkeley students have feelings about them. So when one caught fire fatally last week on the campus, students showed their respects.
It was a delivery robot from the company Kiwi, according to James Wenzel, who posted a tweet along with photos, reporting that students "set up a candlelight vigil for it."